Ryan-Rhys Griffiths
Cited by
Cited by
Constrained Bayesian Optimization for Automatic Chemical Design using Variational Autoencoders
RR Griffiths, JM HernŠndez-Lobato
Chemical Science 11 (2), 577-586, 2020
Mapping Materials and Molecules
B Cheng, RR Griffiths, S Wengert, C Kunkel, T Stenczel, B Zhu, ...
Accounts of Chemical Research 53 (9), 1981-1991, 2020
An Empirical Study of Assumptions in Bayesian Optimisation
AI Cowen-Rivers, W Lyu, R Tutunov, Z Wang, A Grosnit, RR Griffiths, ...
arXiv preprint arXiv:2012.03826, 2021
Adaptive Sensor Placement for Continuous Spaces
JA Grant, A Boukouvalas, RR Griffiths, DS Leslie, S Vakili, EM De Cote
ICML 2019, 2019
The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry
RR Griffiths*, AR Thawani*, A Jamasb, A Bourached, P Jones, ...
arXiv preprint arXiv:2008.03226, 2020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
RR Griffiths, AA Aldrick, M Garcia-Ortegon, VR Lalchand, AA Lee
Machine Learning: Science and Technology, 2021
Gaussian Process Molecule Property Prediction with FlowMO
RR Griffiths*, HB Moss*
NeurIPS 2020: Workshop on ML4Molecules, 2020
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
RR Griffiths, P Schwaller, A Lee, Alpha
NeurIPS 2018: Workshop on ML4Molecules, 2018
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
RR Griffiths*, A Grosnit*, R Tutunov*, AM Maraval*, AI Cowen-Rivers, ...
arXiv preprint arXiv:2106.03609, 2021
Generative Model-Enhanced Human Motion Prediction
A Bourached, RR Griffiths, R Gray, A Jha, P Nachev
NeurIPS 2020: Workshop on Interpretable Inductive Biases and Physically†…, 2020
Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?
A Grosnit, AI Cowen-Rivers, R Tutunov, RR Griffiths, J Wang, ...
Journal of Machine Learning Research, 22(160), 1-78., 2021
Recovery of Underdrawings and Ghost-Paintings via Style Transfer by Deep Convolutional Neural Networks: A Digital Tool for Art Scholars
A Bourached, G Cann, RR Griffiths, DG Stork
Electronic Imaging 2021, 2021
On the Voltage-Controlled Assembly of NP Arrays at Electrochemical Solid/Liquid Interfaces
C Zagar, RR Griffiths, R Podgornik, AA Kornyshev
Journal of Electroanalytical Chemistry 872, 114275, 2018
Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes
RR Griffiths, J Jiang, DJK Buisson, DR Wilkins, LC Gallo, A Ingram, ...
The Astrophysical Journal 914 (2), 2021
Computational Identification of Significant Actors in Paintings through Symbols and Attributes
DG Stork, A Bourached, GH Cann, RR Griffiths
Electronic Imaging 2021, 2021
Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion
A Bourached, R Gray, RR Griffiths, A Jha, P Nachev
arXiv preprint arXiv:2111.12602, 2021
Data Considerations in Graph Representation Learning for Supply Chain Networks
A Aziz, EE Kosasih, RR Griffiths, A Brintrup
ICML 2021: Workshop on ML4Data, 2021
Resolution Enhancement in the Recovery of Underdrawings via Style Transfer by Generative Adversarial Deep Neural Networks
G Cann, A Bourached, RR Griffiths, D Stork
Electronic Imaging 2021, 2021
Supplementary Material for ‘Adaptive Sensor Placement for Continuous Spaces’
JA Grant, A Boukouvalas, RR Griffiths, DS Leslie, S Vakili, EM de Cote
A Theory of a Self-Assembling Electrovariable Smart Mirror
RR Griffiths
arXiv preprint arXiv:1709.05494, 2017
The system can't perform the operation now. Try again later.
Articles 1–20